Biology subjects

Kuan, P. F.

Publications and source records attributed to Kuan, P. F..

5 recordsLinked to original sources

bioRxiv2024

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven for precision genomics.

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bioRxiv2020

RNAAgeCalc: A multi-tissue transcriptional age calculator

We introduced RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator. We utilized GTEx database to identify 1,616 common age-related genes based on meta-analysis of transcriptional age signature across multi-tissues. Additionally, tissue-specific age-related genes were obtained from differential expression analysis on individual tissues. By performing across-tissue transcriptional age prediction, we showed that our 1,616 common age-related genes outperformed other prior age related gene signatures. Furthermore, we utilized TCGA database to demonstrate that the transcriptional age acceleration computed from our within-tissue predictor was significantly correlated with mutation burden, mortality risk and cancer stage. RNAAgeCalc is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#RNAAgeCalc.

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bioRxiv2019

Mixture Network Regularized Generalized Linear Model with Feature Selection

High dimensional genomics data in biomedical sciences is an invaluable resource for constructing statistical prediction models. With the increasing knowledge of gene networks and pathways, such information can be utilized in the statistical models to improve prediction accuracy and enhance model interpretability. However, in certain scenarios the network structure may only be partially known or subject to inaccuracy. Thus, the performance of statistical models incorporating such network structure may be compromised. In this paper, we propose a weighted sparse network learning method by optimally combining a data driven network with sparsity property to prior known or partially known network to address this issue. We show that our proposed model attains the oracle property and achieves a parsimonious structure in high dimensional setting for different types of outcomes including continuous, binary and survival data. Simulations studies show that our proposed model is robust and outperforms existing methods. Case study on melanoma gene expression further demonstrates that our proposed model achieves good operating characteristics in identifying informative genes and predicting survival risk. An R package glmaag implementing our method is available on the Comprehensive R Archive Network (CRAN).

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bioRxiv2019

Negative Binomial Additive Model for RNA-Seq Data Analysis

High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and co-variates of interest. Existing models assume linear effect of covariates, which is restrictive and may not be sufficient for some phenotypes. In this paper, we introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. Based on extensive simulation and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. Our proposed NBAMSeq is available for download at https://github.com/reese3928/NBAMSeq and in submission to Bioconductor repository.

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bioRxiv2018

Multiple-kernel learning for genomic data mining and prediction

Advances in medical technology have allowed for customized prognosis, diagnosis, and personalized treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for integration of multiple high throughput data sources, however, there are currently no implementations of MKL in R. In this paper, we give some background material for support vector machine (SVM) and introduce an R package, RMKL, which provides R and C++ code to implement several MKL algorithms for classification and regression problems. The provided implementations of MKL are compared using benchmark data and TCGA ovarian cancer. We demonstrate that combining multiple data sources can lead to a better classification scheme than simply using a single data source.

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